A 4-year fully-funded PhD studentship with Prof Mark Humphries and Dr JeYoung Yung at the University Of Nottingham is available to start September 2024. The project is titled 'Optimising patient selection for Deep Brain Stimulation in Parkinson’s disease using multimodal machine learning'. The goal of this project is to test how fusing clinical data, neuroimaging, and video assessments could optimise the selection of patients. The project will be in collaboration with MachineMedicine (London), a MedTech company specialising in Parkinson’s disease, and the movement disorders clinical team at St George’s Hospital, London. The PhD student will be trained in data-science and machine learning tools, including how to extract and analyse MRI and fMRI data, in fusing data across modalities, and in developing a machine-learning pipeline for predicting patient outcomes. These predictions will be tested against the 12-month follow-up data from the St George’s trial patients. The student’s further training will include a 3-month placement at MachineMedicine, and visits to St George’s clinic.

The Natural Language Processing for Biomedical Information Analysis (NLP4BIA) group at BSC is an internationally renowned research group working on the development of NLP, language technology, and text mining solutions applied primarily to biomedical and clinical data. It is a highly interdisciplinary team, funded through competitive European and National projects requiring the implementation of natural language processing and advanced AI solutions making use of diverse technologies, including Transformers and recent advances in Large Language Models (LLM) to improve healthcare data analysis. The NLP4BIA-BSC is looking for a Research Engineer with experience in Language Technologies and Deep Learning. The candidate will be involved in technical work related to international projects, being part of a team of researchers working on topics related to clinical Language Models, multilingual NLP, benchmarking of language technology solutions and predictive content mining. The candidate will have the opportunity to advance the state of the art of biomedical language models and NLP methods working in a multidisciplinary environment alongside AI experts, computational linguists, clinical experts, and other engineers.

The Department of Machine Learning for Clinical Neuroscience is currently recruiting PhD candidates and Postdocs. We develop advanced machine and deep learning models to analyze diverse clinical data, including neuroimaging, psychometric, clinical, smartphone, and omics datasets. While focusing on methodological challenges (explainability, robustness, multimodal data integration, causality etc.), the main goal is to enhance early diagnosis, predict disease progression, and personalize treatment for neurological and psychiatric diseases in diverse clinical settings. We offer an exciting and supportive environment with access to state-of-the-art compute facilities, mentoring and career advice through experienced faculty. Hertie AI closely collaborates with the world-class AI ecosystem in Tübingen (e.g. Cyber Valley, Cluster of Excellence “Machine Learning in Science”, Tübingen AI Center).

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.